The classical F-test to compare several population means depends
on the assumption of homogeneity of variance of the population and the normality.
When these assumptions especially the equality of variance is dropped,
the classical F-test fails to reject the null hypothesis even if the data actually
provide strong evidence for it. This can be considered a serious problem
in some applications, especially when the sample size is not large. To deal
with this problem, a number of tests are available in the literature. In this
study, the Brown-Forsythe, Weerahandiís Generalized F, Parametric Bootstrap,
Scott-Smith, One-Stage, One-Stage Range, Welch and Xu-Wangís Generalized
F-tests are introduced and a simulation study is performed to compare
these tests according to type-1 errors and powers in di§erent combinations of
parameters and various sample sizes.